Instrumenting Wireless Sensor Networks for Real-Time Surveillance

1. Instrumenting Wireless Sensor Networks for Real-Time Surveillance Songhwai Oh Advisor: Shankar Sastry EECS UC Berkeley ACCLIMATE

2. Wind Response Of Golden Gate Bridge Fire Response Building Comfort, Smart Alarms Vineyards Instrumenting the world Great Duck Island Redwoods Elder Care Factories ACCLIMATE Soil monitoring

3. service data mgmt network system architecture Challenges = Research Opportunities Monitoring & Managing Spaces and Things applications Store Comm. uRobots actuate MEMS sensing Proc Power technology Miniature, low-power connections to the physical world ACCLIMATE

4. acoustic ultrasound mag mica2dot Challenges = Research Opportunities • Limited capabilities of a sensor node • Limited supply of power • Short communication range • High transmission failure rates • High communication delay rates • Limited amount of memory and computational power • Inaccuracy of sensors • Short sensing range • Low detection probabilities • High false detection probabilities • Inaccuracy of sensor network localization ACCLIMATE

5. Tracking in Sensor Networks • Representative application of sensor networks • Event detection • Communication • Sensor fusion and estimation • Sensor management • Decision making, etc. • Applications • Surveillance and security • Search and rescue • Disaster and emergency response system • Pursuit evasion games • Inventory management • Spatio-temporal data collection • Visitor guidance and other location-based services ACCLIMATE

6. Multiple-Target Tracking (MTT) in Sensor Networks • Model uncertainty • Unknown number of targets • Unknown target initiation and termination times • Measurement noise and inconsistency • Noise, False alarms, Packet losses, Delays • Data association problem • Real-time • Timely outputs required for control applications (e.g., pursuit evasion games) ACCLIMATE

7. Yet Another Complication: Binary Sensors • Why binary sensors? • Sensor output is too noisy to correlate signal strength with range • Simple detection code • 1-bit to communicate • Provides coarse measurements • Difficult to use them directly to initiate, maintain and terminate tracks • We use spatial correlation to fuse binary measurements into finer position measurements • Needs an efficient fusion algorithm ACCLIMATE

8. Problem ACCLIMATE

9. Previous Work: Multiple-Target Tracking (MTT) in Sensor Networks • Traditional – computationally intensive • [Chong et al. 1990] Distributed multitarget multisensor tracking • Classification-based – multiple single-target tracking problems • [Li et al. 2002] Detection, classification and tracking of targets • [Shin et al. 2003] A distributed algorithm for managing multi-target identities in wireless ad-hoc sensor networks • [Liu et al. 2004] Distributed state representation for tracking problems in sensor networks • Ad-hoc – not robust • [Liu et al. 2003] Distributed group management for track initiation and maintenance in target localization applications • No general algorithm suited to sensor networks ACCLIMATE

10. Outline • Multiple-target tracking (MTT) algorithm • Multi-sensor fusion algorithm • Markov chain Monte Carlo data association (MCMCDA) • Results from the final experiment of the Network Embedded Systems Technology (NEST) project ACCLIMATE

11. Overall Architecture Controller MTT Fusion MCMCDA ACCLIMATE

12. “Simple” Multi-Sensor Fusion ACCLIMATE

13. Multi-Sensor Fusion: Likelihood Detections Likelihood ACCLIMATE

14. Multi-Sensor Fusion: Threshold • But it requires detections from all sensors to account false alarms • Instead we compute the likelihood if there are at least nd detections Likelihood Likelihood after threshold ACCLIMATE

15. Multi-Sensor Fusion: Position Estimation Black circle: position estimate ACCLIMATE

16. Overall Architecture Controller MTT Fusion MCMCDA ACCLIMATE

17. MTT Problem: General Setup ACCLIMATE

18. Solution Space of Data Association Problem (a) Observations Y (b) Example of a partition  of Y ACCLIMATE

19. Two Possible Solutions to Data Association Problem ACCLIMATE

20. Markov Chain Monte Carlo (MCMC) • A general method to generate samples from a complex distribution • For some complex problems, MCMC is the only known general algorithm that finds a good approximate solution in polynomial time [Jerrum, Sinclair, 1996] • Applications: • Complex probability distribution integration problems • Counting problems (#P-complete problems) • Combinatorial optimization problems • Data association problem has a very complex probability distribution ACCLIMATE

21. MCMC Data Association (MCMCDA)* • Start with some initial state 12  ACCLIMATE *[Oh, Russell, Sastry 2004]

22. MCMC Data Association (MCMCDA) • Propose a new state ’» q(n,’) • q: £ 2! [0,1], proposal distribution q(n,’) = probability of proposing ’ when the chain is in n ’ n propose • q(n,’) is determined by 8 moves: ACCLIMATE

23. MCMC Data Association (MCMCDA) • If accepted, • Accept the proposal with probability () = P(|Y), Y = observations n+1=’ • If not accepted, n+1=n ACCLIMATE

24. MCMC Data Association (MCMCDA) • Repeat it for N steps  ACCLIMATE

25. MCMC Data Association (MCMCDA) • Repeat it for N steps  ACCLIMATE

26. MCMC Data Association (MCMCDA) • Repeat it for N steps  ACCLIMATE

27. Optimality in the Limit But how fast does it converge? ACCLIMATE

28. Polynomial-Time Approximation to Joint Probabilistic Data Association* ACCLIMATE *[Oh, Sastry 2005]

29. Controller MTT Fusion MCMCDA Overall Architecture Multi-agent coordination algorithm • Minimize time to capture all evaders • Robust Minimum Time Control (MTC) ACCLIMATE

30. Simulation: Multiple-Target Tracking & Pursuit Evasion Games in Sensor Networks ACCLIMATE

31. NEST Final Experiment: MTT Demo • Goal • Track an unknown number of multiple targets using a sensor network of binary sensors without classification information • Coordinate multiple pursuers to chase and capture multiple evaders in minimum time using a sensor network • Done in simulation due to physical and time constraints ACCLIMATE

32. NEST Final Experiment: Summer 2005 ACCLIMATE

33. NEST Final Experiment: Sensor Node • Telos B mote • 8MHz TI MSP430 microcontroller • RAM: 10kB; Flash: 48kB • Chipcon CC2420 Radio: 250kbps, 2.4GHz, IEEE 802.15.4 standard compliant • Radio range of up to 125 meters • Trio Sensor Board • Features a microphone, a piezoelectric buzzer, x-y axis magnetometers, and four passive infrared (PIR) motion sensors • Solar-power charging circuitry Trio Node ACCLIMATE

34. NEST Final Experiment: System • Software • TinyOS • Deluge • Network reprogramming • Drip and Drain (Routing Layer) • Drip: disseminate commands • Drain: collect data • DetectionEvent • Multi-moded event generator • Multi-sensor fusion and multiple-target tracking algorithms ACCLIMATE

35. NEST Final Experiment: Demo ACCLIMATE

36. Current and Future Work • Sensor networks • Robust distributed tracking algorithm • Robust tracking against malicious attacks • Performance analysis and metrics for sensor networks • Camera networks • Distributed multiple-target tracking and identity management ACCLIMATE

37. Distributed multiple-target tracking and identity management*: an application of MCMCDA ACCLIMATE *[Oh, Hwang, Roy, Sastry, 2005]

38. Summary • Sensor networks • Individual sensor nodes are incapable and inaccurate • But the aggregation of spatially spread sensors can provide accurate estimates using spatio-temporal correlation • System-level approach • Multi-sensor fusion may provide incorrect and inconsistent position reports • The inconsistency in position reports are fixed by the MCMCDA tracking algorithm using temporal correlation • Adaptive control system ACCLIMATE